A deep comprehension of the development of scientific knowledge is necessary to properly appreciate technological advancement and the process of knowledge innovation. However, finegrained citation networks are less frequently utilized to characterize the evolution of scientific knowledge. To bridge this gap, we first constructed the fine-grained citation networks based on the PubMed and Web of Science (WOS) databases, then extracted the ego-centered networks of scientific knowledge, and finally employed Exponential Random Graph Models (ERGMs) to analyze the factors influencing the formation of...
